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Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
Sanger Sequencing01:57

Sanger Sequencing

DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...

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Related Experiment Video

Updated: Jun 16, 2026

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Using hidden Markov models to align multiple sequences.

David W Mount

    Cold Spring Harbor Protocols
    |February 12, 2010
    PubMed
    Summary
    This summary is machine-generated.

    Hidden Markov models (HMMs) offer a probabilistic approach to protein multiple sequence alignment (MSA). This study explores HMM advantages, disadvantages, and algorithms for optimal model creation.

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    Area of Science:

    • Computational Biology
    • Bioinformatics
    • Statistical Modeling

    Background:

    • Protein multiple sequence alignment (MSA) is crucial for understanding protein function and evolution.
    • Hidden Markov models (HMMs) provide a probabilistic framework for sequence analysis.
    • Existing MSA methods may not fully capture complex evolutionary relationships.

    Purpose of the Study:

    • To evaluate the strengths and weaknesses of HMMs for protein MSA.
    • To present algorithms for HMM construction and optimization.
    • To identify conditions for generating the most effective HMMs.

    Main Methods:

    • Utilizing probabilistic state transitions to model sequence alignments.
    • Employing Markov chains to represent movement through alignment states.
    • Calculating state and transition probabilities for sequence matching.

    Main Results:

    • HMMs represent MSA columns as symbol frequency distributions (states).
    • Insertions and deletions are modeled using distinct states within the HMM.
    • The probability of a sequence is derived from multiplying state and transition probabilities.

    Conclusions:

    • HMMs offer a powerful, albeit complex, method for protein MSA.
    • Understanding HMM algorithms is key to maximizing their utility.
    • Further research into HMM optimization can enhance biological sequence analysis.